title: Prioritization of epilepsy associated candidate genes by convergent analysis.
abstract: BACKGROUND
  Epilepsy is a severe neurological disorder affecting a large number of individuals, yet the underlying genetic risk factors for epilepsy remain unclear. Recent studies have revealed several recurrent copy number variations (CNVs) that are more likely to be associated with epilepsy. The responsible gene(s) within these regions have yet to be definitively linked to the disorder, and the implications of their interactions are not fully understood. Identification of these genes may contribute to a better pathological understanding of epilepsy, and serve to implicate novel therapeutic targets for further research.
  
  
  RESULTS
  In this study, we examined genes within heterozygous deletion regions identified in a recent large-scale study, encompassing a diverse spectrum of epileptic syndromes. By integrating additional protein-protein interaction data, we constructed subnetworks for these CNV-region genes and also those previously studied for epilepsy. We observed 20 genes common to both networks, primarily concentrated within a small molecular network populated by GABA receptor, BDNF/MAPK signaling, and estrogen receptor genes. From among the hundreds of genes in the initial networks, these were designated by convergent evidence for their likely association with epilepsy. Importantly, the identified molecular network was found to contain complex interrelationships, providing further insight into epilepsy's underlying pathology. We further performed pathway enrichment and crosstalk analysis and revealed a functional map which indicates the significant enrichment of closely related neurological, immune, and kinase regulatory pathways.
  
  
  CONCLUSIONS
  The convergent framework we proposed here provides a unique and powerful approach to screening and identifying promising disease genes out of typically hundreds to thousands of genes in disease-related CNV-regions. Our network and pathway analysis provides important implications for the underlying molecular mechanisms for epilepsy. The strategy can be applied for the study of other complex diseases.
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.
authors.forename: Peilin
authors.initials: P
authors.lastname: Jia
authors.forename: Jeffrey M
authors.initials: JM
authors.lastname: Ewers
authors.forename: Zhongming
authors.initials: Z
authors.lastname: Zhao
mesh_terms: D056915:DNA Copy Number Variations / Q000502:physiology; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D004827:Epilepsy / Q000235:genetics*; D018628:Gene Dosage; D053263:Gene Regulatory Networks; D005796:Genes / Q000502:physiology; D056726:Genetic Association Studies / Q000379:methods* / Q000706:statistics & numerical data*; D020022:Genetic Predisposition to Disease; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics; D008954:Models, Biological; D011963:Receptors, GABA-A / Q000235:genetics; D011978:Receptors, Nicotinic / Q000235:genetics; D012106:Research; D064569:alpha7 Nicotinic Acetylcholine Receptor
chemical_list: C579167:Chrna7 protein, human; C546240:GABRA1 protein, human; D011963:Receptors, GABA-A; D011978:Receptors, Nicotinic; D064569:alpha7 Nicotinic Acetylcholine Receptor